Convolutional neural networks for brain tumour segmentation
Bhandari, Abhishta, Koppen, Jarrad, Agzarian, Marc, and UNSPECIFIED (2020) Convolutional neural networks for brain tumour segmentation. Insights Into Imaging, 11. 77.
|
PDF (Published Version)
- Published Version
Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
The introduction of quantitative image analysis has given rise to fields such as radiomics which have been used to predict clinical sequelae. One growing area of interest for analysis is brain tumours, in particular glioblastoma multiforme (GBM). Tumour segmentation is an important step in the pipeline in the analysis of this pathology. Manual segmentation is often inconsistent as it varies between observers. Automated segmentation has been proposed to combat this issue. Methodologies such as convolutional neural networks (CNNs) which are machine learning pipelines modelled on the biological process of neurons (called nodes) and synapses (connections) have been of interest in the literature. We investigate the role of CNNs to segment brain tumours by firstly taking an educational look at CNNs and perform a literature search to determine an example pipeline for segmentation. We then investigate the future use of CNNs by exploring a novel field-radiomics. This examines quantitative features of brain tumours such as shape, texture, and signal intensity to predict clinical outcomes such as survival and response to therapy.
| Item ID: | 63677 |
|---|---|
| Item Type: | Article (Research - C1) |
| ISSN: | 1869-4101 |
| Keywords: | Glioblastoma, Convolutional neural network, Artificial intelligence, Segmentation |
| Copyright Information: | © The Author(s). 2020Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License,which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you giveappropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtainpermission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/ |
| Date Deposited: | 01 Jul 2020 07:31 |
| FoR Codes: | 32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320226 Surgery @ 30% 32 BIOMEDICAL AND CLINICAL SCIENCES > 3211 Oncology and carcinogenesis > 321199 Oncology and carcinogenesis not elsewhere classified @ 30% 32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320222 Radiology and organ imaging @ 40% |
| Downloads: |
Total: 790 Last 12 Months: 11 |
| More Statistics |
Actions (Repository Staff Only)
 |
Item Control Page |